Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Abstract

Millimeter-Wave (mmWave) technology is deemed as a feasible approach for future vehicular communications. However, mmWave signals are characterized by high path loss and penetration loss, which can be alleviated by directional communication. Directional transmission performance depends on beam alignment between transmitter and receiver, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this type of works does not take energy efficiency into account. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall, where the power consumption can be reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results show that the EFML scheme improves both the network energy efficiency and the amount data of cellular-assisted vehicular networks at the cost of more beam performance update overhead. However, there is no difference in the cost of updating beam performance after adequate online learning.

A Reduced Complexity Hybrid Precoding for mmWave Systems

“…Nowadays,there is increasing research in the area of mmWave MIMO systems ( [5,6,7,8,9,10,11,12,13,14]). In this work, we propose low-complexity channel decomposition precoding techniques for a mmWave system with large antenna arrays at both the base station (BS) and mobile station MS in this research.…”